The classical model of adaptive evolution in an asexual population postulates that each adaptive clone is derived from the one preceding it1. However, experimental evidence suggests more complex dynamics2-5 with theory predicting the fixation probability of a beneficial mutation as dependent on the mutation rate, population size, and the mutation's selection coefficient6. Clonal interference has been demonstrated in viruses7 and bacteria8, but has not been demonstrated in a eukaryote and a detailed molecular characterization is lacking. Here we use different fluorescent markers to visualize the dynamics of asexually evolving yeast populations. For each adaptive clone within one of our evolving populations, we have identified the underlying mutations, monitored their population frequencies and used microarrays to characterize changes in the transcriptome. These data provide the most detailed molecular characterization of an experimental evolution to date, and provide direct experimental evidence supporting both the clonal interference and the multiple mutation models.

This study characterized the transcript profile of Escherichia coli in acetate cultures using DNA microarray on glass slides. Glucose-grown cultures were used as a reference. At the 95% confidence level, 354 genes were up-regulated in acetate, while 370 genes were downregulated compared with the glucose-grown culture. Generally, more metabolic genes were up-regulated in acetate than other gene groups, while genes involved in cell replication, transcription, and translation machinery tended to be down-regulated. It appears that E. coli commits more resources to metabolism at the expense of growth when cultured in the poor carbon source. The expression profile confirms many known features in acetate metabolism such as the induction of the glyoxylate pathway, tricarboxylic acid cycle, and gluconeogenic genes. It also provided many previously unknown features, including induction of malic enzymes, ppsA, and the glycolate pathway and repression of glycolytic and glucose phosphotransferase genes in acetate. The carbon flux delivered from the malic enzymes and PpsA in acetate was further confirmed by deletion mutations. In general, the gene expression profiles qualitatively agree with the metabolic flux changes and may serve as a predictor for gene function and metabolic flux distribution.Physiological characteristics of Escherichia coli using acetate or glucose as a sole carbon and energy source have been studied for more than three decades (1, 2). Briefly, E. coli uptakes glucose using the phosphotransferase system that converts extracellular glucose into intracellular glucose 6-phosphate, which can be further metabolized by the glycolytic pathway to produce energy and biosynthetic precursors. In the presence of glucose, the adenylate cyclase is inactive, and the cAMP level is low. In the absence of glucose, the adenylate cyclase is activated to produce cAMP, which when binding to the cAMP receptor protein activates the expression of a large set of catabolite derepression genes (2, 3). On the other hand, acetate is transported into the cell and converted to acetyl-CoA, which is further metabolized through the glyoxylate shunt and the tricarboxylic acid cycle. The acetate-metabolizing genes are typically repressed in the presence of glucose. The induction and regulation of acetate-metabolizing genes have been studied extensively (4). Since the two carbon sources, glucose and acetate, are utilized by distinct metabolic pathways, the metabolic flux distribution differs significantly in these two carbon sources (5). Understanding global gene expression profiles in different carbon sources is important to the investigation of E. coli growth in natural environment, where the availability of carbon sources changes dynamically. Acetate-metabolizing culture is particularly relevant to the biotechnology industry, since the accumulation of acetate in bioreactor is commonly observed and often poses as an obstacle to high cell density cultivation (6).The recent advent of microarray technology allows a thorough analysis of gene ex...

Cells adjust gene expression profiles in response to environmental and physiological changes through a series of signal transduction pathways. Upon activation or deactivation, the terminal regulators bind to or dissociate from DNA, respectively, and modulate transcriptional activities on particular promoters. Traditionally, individual reporter genes have been used to detect the activity of the transcription factors. This approach works well for simple, nonoverlapping transcription pathways. For complex transcriptional networks, more sophisticated tools are required to deconvolute the contribution of each regulator. Here, we demonstrate the utility of network component analysis in determining multiple transcription factor activities based on transcriptome profiles and available connectivity information regarding network connectivity. We used Escherichia coli carbon source transition from glucose to acetate as a model system. Key results from this analysis were either consistent with physiology or verified by using independent measurements. B acteria respond to environmental changes through a variety of sensor proteins, which eventually relay the signals to corresponding DNA binding proteins to modulate transcription. The DNA binding transcription regulators, or transcription factors (TFs), typically require posttranscriptional modification or ligand binding to assume an active conformation, which may bind to DNA and either positively or negatively regulate transcription. Here, the activity of a TF is defined as the concentration of its subpopulation capable of DNA binding. The collective activities of TFs can thus be regarded as the physiological state of the cell. Determining these TF activities (TFAs) allows better understanding of how cells respond to changes in the environment. Careful experimental studies in the past few decades have identified conditions that perturb each individual TF independent of others. Although such ideal conditions allowed useful characterization of molecular mechanisms, most environmental perturbations are complex and are likely to provoke multiple regulatory systems simultaneously. Without a proper method of decomposing the regulatory signals, it is difficult to investigate how microorganisms coordinate various regulatory pathways upon an environmental challenge.Here, we report the use of network component analysis (NCA) recently developed in our group (1) to determine the dynamics of the activities of various TFs during a physiological process. This approach uses both DNA microarray data and partial information regarding the membership of regulons as defined by each TF in question. It contrasts with other approaches, such as singular value decomposition (2) or independent component analysis (3), in that it does not depend on orthogonality and statistical independence. Rather, it uses biological information regarding regulatory network topology, even when the topology is incompletely defined. Furthermore, NCA differs from modelbased parameter estimation (4) because it allows deconv...

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Backgroundn-Butanol is a promising emerging biofuel, and recent metabolic engineering efforts have demonstrated the use of several microbial hosts for its production. However, most organisms have very low tolerance to n-butanol (up to 2% (v/v)), limiting the economic viability of this biofuel. The rational engineering of more robust n-butanol production hosts relies upon understanding the mechanisms involved in tolerance. However, the existing knowledge of genes involved in n-butanol tolerance is limited. The goal of this study is therefore to identify E. coli genes that are involved in n-butanol tolerance.Methodology/Principal FindingsUsing a genomic library enrichment strategy, we identified approximately 270 genes that were enriched or depleted in n-butanol challenge. The effects of these candidate genes on n-butanol tolerance were experimentally determined using overexpression or deletion libraries. Among the 55 enriched genes tested, 11 were experimentally shown to confer enhanced tolerance to n-butanol when overexpressed compared to the wild-type. Among the 84 depleted genes tested, three conferred increased n-butanol resistance when deleted. The overexpressed genes that conferred the largest increase in n-butanol tolerance were related to iron transport and metabolism, entC and feoA, which increased the n-butanol tolerance by 32.8±4.0% and 49.1±3.3%, respectively. The deleted gene that resulted in the largest increase in resistance to n-butanol was astE, which enhanced n-butanol tolerance by 48.7±6.3%.Conclusions/SignificanceWe identified and experimentally verified 14 genes that decreased the inhibitory effect of n-butanol tolerance on E. coli. From the data, we were able to expand the current knowledge on the genes involved in n-butanol tolerance; the results suggest that an increased iron transport and metabolism and decreased acid resistance may enhance n-butanol tolerance. The genes and mechanisms identified in this study will be helpful in the rational engineering of more robust biofuel producers.